Cross-correlation based echo canceller controllers

ABSTRACT

Cross-correlation based echo canceller controllers are described herein. By way of example, a system for controlling an echo canceller having one or more adaptive filters can include one or more adaptive filter controllers each corresponding to one of the one or more adaptive filters and each configured to halt adaptation of its corresponding adaptive filter according to the cross-correlation of its corresponding corrupted signal and its corresponding error signal of its corresponding adaptive filter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to co-pending U.S. patent application Ser.No. 11/695,440, entitled HYBRID ECHO CANCELLER CONTROLLERS, filed onApr. 2, 2007, and U.S. patent application Ser. No. 11/695,447, entitledHYBRID ECHO CANCELLER CONTROLLERS, filed on Apr. 2, 2007.

BACKGROUND

Acoustic echo cancellation (AEC) algorithms are used to suppress theecho from a loudspeaker(s) that can be captured by a microphone(s)located in close proximity. Typically, AEC is used during full-duplexcommunication between someone located in a near-end room speaking withanother person located remotely in a far-end room. When the far-endperson speaks, their voice is played through the speakers in thenear-end room. The echo from the far-end person's speech is thencaptured by the near-end microphone(s). Without AEC, the far-end speechecho would be transmitted back to the far-end and the far-end personwould hear a delayed echo of their previous speech out of the speakersin the far-end room.

When far-end speech is played from loudspeakers located in the near-endroom simultaneously when there is near-end speech, the condition iscommonly referred to as doubletalk. If the coefficients for the AEC'sadaptive filters are updated when there is any near-end speech or othertransient acoustic signal in the near-end room or when there isdoubletalk, the adaptive filters will converge in such a manner as tocancel part of the near-end speech in addition to the echo from theloudspeaker. Cancellation of the near-end speech leads to distortion ofthe processed speech signal and should be avoided.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

The subject matter described herein facilitates controlling theperformance of an echo canceller (EC), for instance an acoustic echocanceller (AEC) or a line echo canceller (LEC). By way of example, thesubject matter includes controllers that cross-correlate a microphonesignal and the EC's error signal in order to control the adaptation ofthe EC. By way of another example, the subject matter described hereinincludes controllers that combine cross-correlation with techniques todiscriminate near-end signal from echo in order to control theadaptation of the EC. The cross-correlation measure can include, forinstance, a measure based on cross-correlating the microphone signal andthe EC's cancellation error signal. By way of another example, thesubject matter described herein also includes controllers that employtwo or more cross-correlation measures in order to control theadaptation of an EC.

The following description and the annexed drawings set forth in detailcertain illustrative aspects of the subject matter. These aspects areindicative, however, of but a few of the various ways in which thesubject matter can be employed and the claimed subject matter isintended to include all such aspects and their equivalents. Although thedrawings illustrate the specific environment for an AEC, the subjectmatter described herein is applicable to any EC, including line echocancellers (LEC).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an echo canceller (EC) implemented inthe time domain.

FIG. 2 schematically illustrates an echo canceller (EC) implemented inthe frequency domain.

FIG. 3 schematically illustrates another example of an echo canceller(EC).

FIG. 4 is a graph showing the performance of the XECC and MECC detectors(acronyms explained below).

FIG. 5 is a graph showing the performance of the MECC decision statistic(acronyms explained below).

FIG. 6 is a graph showing the performance of the MECC decision statistic(acronyms explained below).

FIG. 7 is a block diagram of one example of a system for controlling anEC.

FIG. 8 is a block diagram of another example of a system for controllingan EC.

FIG. 9 is a flow diagram of one example of a method for declaring anear-end signal.

FIG. 10 is a flow diagram of one example of a method for controlling anEC.

FIG. 11 is a flow diagram of another example of a method for controllingan EC.

FIG. 12 is a flow diagram of yet another example of a method forcontrolling an EC.

FIG. 13 is a block diagram of another example of a system forcontrolling an EC.

FIG. 14 is a block diagram of another example of a system forcontrolling an EC.

FIGS. 15A-C are block diagrams of other examples of a system forcontrolling an EC.

FIG. 16 is a graph showing the convergence of the MECC and XMCCdetectors (acronyms explained below).

FIG. 17 is a graph showing the performance of the MECC, XMCC, RTRL andHybrid detectors (acronyms explained below).

FIG. 18 is a block diagram of another example of a system forcontrolling an EC.

FIG. 19 is a block diagram of another example of a system forcontrolling an EC.

FIG. 20 is a flow diagram of an example of a method for declaring anear-end signal.

FIG. 21 is a flow diagram of another example of a method for controllingan EC.

FIG. 22-29 are block diagrams of other examples of a system forcontrolling an EC.

FIG. 30 is a flow diagram of another example of a method of controllingan EC.

FIG. 31 is a block diagram of another example of a system forcontrolling an EC.

FIG. 32 is a block diagram of another example of a system forcontrolling an EC.

FIG. 33 is a flow diagram of another example of a method of controllingan EC.

DETAILED DESCRIPTION

Although the subject matter described herein may be described in thecontext of teleconferencing, acoustic echo cancellation and line echocancellation, the subject matter is not limited to these applications.Rather, the signals to be detected and the signals to be cancelledinclude any suitable type of signal such as music or sounds from videogames.

Many teleconferencing conversations are conducted in the presence ofacoustic echoes. An acoustic echo canceller (AEC) can be used to removethe echo created due to the loudspeaker-microphone proximity. AECadaptive filters can be implemented in the time domain (FIG. 1) and thefrequency domain (FIG. 2). An AEC adaptively synthesizes a replica ofthe echo and subtracts it from the echo-corrupted microphone signal.When the near-end talker is active or when the speech comes from boththe far-end and the near-end, the filter coefficients will diverge fromthe true echo path impulse response if adaptation is enabled. A speechdetector can be used to avoid this by stopping the AEC's adaptationduring periods of near-end speech. In addition, adaptation can be haltedduring periods when both near-end speech and echo are absent.

Similarly, line echo cancellation (LEC) algorithms can be used tosuppress the echo from a microphone signal that is caused by hybridcircuits in telephone networks and telecommunication equipment. Althoughsome of the subject matter described herein may be described in terms ofacoustic echo cancellation, the subject matter also is applicable toLEC. For AEC, the signal at the microphone can be referred to as thecorrupted signal, which can include near-end signal (e.g., speech) withor without echo. The loudspeaker signal in the near-end room can bereferred to as the reference signal and the echo in the case of AEC isthe echo from the loudspeaker sound. For LEC performed in atelecommunications device such as a telephone or video conferencingsystem, the signal input to the telephone speaker can be referred to asthe corrupted signal, which can include sounds such as speech (near-endsignal) with or without microphone echo. The microphone signal can bereferred to as the reference signal. For LEC performed in the network,the reference signal is the far-end signal and the corrupted signal isthe near-end signal plus the background noise and the echo generated bythe far-end signal due to impedance mismatch in 2-wire/4-wireconverters.

Ideally, a near-end signal detection algorithm should be able to detecta near-end signal condition quickly and accurately so as to freezeadaptation as soon as possible, track any echo-path changes anddistinguish near-end signal from the echo-path variations. One exampleof a decision variable for near-end signal detection behaves as follows:

(1) If near-end signal is not present i.e. v=0, then ζ≧R_(Th); and

(2) If near-end signal is present i.e. v≠0, then ζ<R_(Th).

It is desirable for the threshold R_(Th) to be a constant independent ofthe data and for the decision statistic ζ to be insensitive to echo-pathvariations when v=0. Moreover, it is desirable that decisions are madewithout introducing any undue delay; since delayed decisions adverselyaffect the performance of an echo canceller (EC).

FIG. 3 shows the basic structure of an adaptive echo canceller (EC) inthe time domain. The reference signal x is filtered through the roomimpulse response h to get the echo signal:y(n)=h ^(T) xwhereh=[h ₀ h ₁ . . . , h _(L-1)]^(T),x=[x(n)x(n−1) . . . , x(n−L+1)]^(T),and L is the length of the echo-path. This echo signal is added to thenear-end signal signal v to yield the corrupted signal:m(n)=y(n)+v(n)The error signal at time n is:e(n)=m(n)−ĥ ^(T) xThis error signal is used to adapt the L taps of the adaptive AEC filterĥ.

Others have proposed using the cross-correlation vector between thereference signal vector x (which is played out of the speakers for AEC)and the AEC's cancellation error e, r_(ex)=E[ex^(T)], as the basis fornear-end signal detection (XECC algorithm). Simulation results indicatethat this approach does not work well for detecting near-end signal, anda theoretical derivation provides further insight. Noting that thenear-end signal v is independent of the reference signal x and assumingall of the signals are zero mean, the cross-correlation between theAEC's error signal and the reference signal is:

$\begin{matrix}{r_{ex} = {E\lbrack {( {y + v - {{\hat{h}}^{T}x}} )x^{T}} \rbrack}} \\{= {E\lbrack {( {{h^{T}x} - {{\hat{h}}^{T}x}} )x^{T}} \rbrack}} \\{= {( {h^{T} - {\hat{h}}^{T}} )R_{xx}}}\end{matrix}$where E[●] denotes the mathematical expectation and R_(xx)=E[xx^(T)].From the above, it is apparent that r_(ex) is high only when there is achange in the echo-path; hence the XECC is more suitable for trackingecho-path variations rather than detecting near-end signal. When theXECC is high, echo only (no near-end signal) with echo-path change isconsidered present in the corrupted signal. When the XECC is low,near-end signal, doubletalk, echo only with no echo-path change or noiseonly can be present in the corrupted signal. Thus, the XECC cannotdistinguish between near-end signal and only noise in the corruptedsignal, so it cannot detect near-end signal.

Others also have proposed an algorithm based on the cross-correlationbetween the reference signal vector x and the corrupted signal scalar m,r_(xm)=E[xm] (XMCC algorithm). The XMCC decision statistic is given byζ_(XMCC)=√{square root over (r _(xm) ^(T)(σ_(m) ² R _(xx))⁻¹ r _(xm))}where R_(xx) is defined as above and the variance of the corruptedsignal (σ_(m) ²) is

$\begin{matrix}{\sigma_{m}^{2} = {E\lbrack {m\; m^{T}} \rbrack}} \\{= {E\lbrack {( {y + v} )( {y + v} )^{T}} \rbrack}} \\{= {{E\lbrack {yy}^{T} \rbrack} + {E\lbrack {vv}^{T} \rbrack}}} \\{= {{E\lbrack {h^{T}{x( {h^{T}x} )}^{T}} \rbrack} + \sigma_{v}^{2}}} \\{= {{h^{T}R_{xx}h} + {\sigma_{v}^{2}.}}}\end{matrix}$When the XMCC is high, echo only with or without echo path change isconsidered present in the corrupted signal. When the XMCC is low,near-end signal, doubletalk or only noise are present in the corruptedsignal. Thus, the XMCC cannot distinguish near-end signal from echo-pathvariation or only noise.

Instead of using r_(ex) or r_(xm) as discussed above, thecross-correlation between the corrupted signal m and the cancellationerror e, r_(em)=E[em], can be used as the basis for signal detection(MECC algorithm). By way of example, the decision statistic can bedefined to be

$\xi_{MECC} = {1 - {\frac{r_{em}}{\sigma_{m}^{2}}.}}$The cross-correlation between the corrupted signal and the cancellationerror is

$\quad\begin{matrix}{r_{em} = {E\lbrack {( {y + v - {{\hat{h}}^{T}x}} )( {y + v} )^{T}} \rbrack}} \\{= {E\lbrack {( {{h^{T}x} - {{\hat{h}}^{T}x} + v} )( {{h^{T}x} + v^{T}} \rbrack} }} \\{= {E\lbrack {{( {{h^{T}x} - {{\hat{h}}^{T}x}} )x^{T}h} + {vv}^{T}} \rbrack}} \\{= {{( {h^{T} - {\hat{h}}^{T}} )R_{xx}h} + \sigma_{v}^{2}}}\end{matrix}$where σ_(v) ² is the near-end signal power, and the reference signalvector x and the near-end signal v are independent and are assumed to bezero mean. Substituting equations yields:

$\quad\begin{matrix}{\xi_{MECC} = {1 - \frac{{( {h^{T} - {\hat{h}}^{T}} )R_{xx}h} + \sigma_{v}^{2}}{{h^{T}R_{xx}h} + \sigma_{v}^{2}}}} \\{= {\frac{{\hat{h}}^{T}R_{xx}h}{{h^{T}R_{xx}h} + \sigma_{v}^{2}}.}}\end{matrix}$for v=0, ζ_(MECC)≈1 and for v≠0, ζ_(MECC)<1.

The values for r_(em) and σ_(m) are not available in practice. As aresult, a practical decision statistic is:

${\xi_{MECC} = {1 - \frac{{\hat{r}}_{em}}{{\hat{\sigma}}_{m}^{2}}}},$which is based on either the sample estimates {circumflex over(r)}_(em)(n) and {circumflex over (σ)}_(m) ²(n) or the frame estimates{circumflex over (r)}_(em)(t) and {circumflex over (σ)}_(m) ²(t). Thesample estimates for time sample n can be found, for instance, by usingan exponential recursive weighting algorithm as:{circumflex over (r)} _(em)(n)=λ{circumflex over (r)}_(em)(n−1)+(1−λ)e(n) m(n){circumflex over (σ)}_(m) ²(n)=λ{circumflex over (σ)}_(m) ²(n−1)+(1−λ)m² (n)where e(n) is the cancellation error at time sample n, m(n) is thecaptured corrupted signal sample at the time sample n, and λ is anexponential weighting factor. In another example, the frame estimatesfor time frame t can also be found, for instance, by using anexponential recursive weighting algorithm, which is the maxima of thecorrelation in a frame as:{circumflex over (r)}_(em)(t)=λ{circumflex over(r)}_(em)(t−1)+(1−λ)e(t)m ^(T)(t){circumflex over (σ)}_(m) ²(t)=λ{circumflex over (σ)}_(m)²(t−1)+(1−λ)m(t)m ^(T)(t)where e(t) is the cancellation error vector in the time frame t, m(t) isthe captured corrupted signal vector at the time frame t, and λ is anexponential weighting factor.

The previous estimates of the corrupted signal variance (e.g.{circumflex over (σ)}_(m) ²(n), {circumflex over (σ)}_(m) ²(t)) assumethat the corrupted signal has zero mean. If the corrupted signal doesnot have zero mean, the mean can also be recursively estimated andincorporated in the corrupted signal variance estimate. In one examplefor the time domain, sample based decision statistic:{circumflex over (μ)}_(m)(n)=λ{circumflex over (μ)}_(m)(n−1)+(1−λ)m(n){circumflex over (σ)}_(m) ²(n)=λ{circumflex over (σ)}_(m)²(n−1)+(1−λ)(m(n)−{circumflex over (μ)}_(m)(n))²where {circumflex over (μ)}_(m)(n) is the sample based estimate of themean of the corrupted signal. In another example for the time domain,frame based decision statistic:

${{\hat{\mu}}_{m}(t)} = {{\lambda\;{{\hat{\mu}}_{m}( {t - 1} )}} + {\frac{( {1 - \lambda} )}{L}{\sum\limits_{l = 0}^{L - 1}{m_{i}(t)}}}}$σ̂_(m)²(t) = λ σ̂_(m)²(t − 1) + (1 − λ)(m(t) − μ̂_(m)(t))(m(t) − μ̂_(m)(t))^(T)where {circumflex over (μ)}_(m)(t) is the frame based estimate of themean of the corrupted signal, and m_(l)(t) is the lth element in thecorrupted signal vector of length L. Since smaller values of λ yieldbetter time varying signal tracking capability at the expense of worseestimation accuracy, for slowly time varying signals, 0.9≦λ≦1 can bechosen. When ζ_(MECC)<R_(Th), the captured frame of the corrupted signalcan be considered to have near-end signal present and adaptation of theEC's adaptive filter(s) is halted. Otherwise, adaptation is continued.Although an MECC algorithm can be employed in this manner, the MECCdecision statistic will be low when near-end signal is absent and echois present (echo only) if echo-path change also is present. The samplebased and frame based decision statistics can further include a noiseterm that is an indication of the noise in the corrupted signal, forinstance, an estimate of the power of the noise. In one example, thedecision statistic including the noise term can be:

$\xi_{MECC} = {1 - \frac{{\hat{r}}_{em} - {\hat{\sigma}}_{z}^{2}}{{\hat{\sigma}}_{m}^{2}}}$where {circumflex over (σ)}_(z) ² is the variance of the noise in thenth sample ({circumflex over (σ)}_(z) ²(n)) for the sample baseddecision statistic or is the variance of the noise in the rth frame({circumflex over (σ)}_(z) ²(t)) for the frame based decisionsstatistic. In another example, the decision statistic including thenoise term can be:

$\xi_{MECC} = {1 - \frac{{\hat{r}}_{em} - {\hat{\sigma}}_{z}^{2}}{{\hat{\sigma}}_{m}^{2} - {\hat{\sigma}}_{z}^{2}}}$There are other methods of estimating the cross-correlation coefficientand the variance of the corrupted signal and any suitable method can beused.

In addition to its simplicity, another advantage of the MECC algorithmis that only the maximum cross-correlation needs to be computed insteadof computing the entire cross-correlation vector as is required by theother algorithms. This results in significant computational savingscompared to the other algorithms, requiring only 2l multiplications, 3additions and a division to compute the decision statistic, where l=256samples is the frame size for 16 kHz sample rate and 16 msec frames.

The MECC and the XMCC decision statistics are different in that theformer is based on r_(em) and the latter is based on r_(xm).Substituting r_(xm)=R_(xx)h and σ_(m) ²=h^(T)R_(xx)h+σ_(v) ² in the XMCCdecision statistic equation above yields:

$\quad\begin{matrix}{\xi_{XMCC}^{2} = {h^{T}{R_{xx}( {\sigma_{m}^{2}R_{xx}} )}^{- 1}R_{xx}h}} \\{{= \frac{h^{T}R_{xx}h}{{h^{T}R_{xx}h} + \sigma_{v}^{2}}},}\end{matrix}$whereas the MECC decision statistic is given by:

$\xi_{MECC} = {\frac{{\hat{h}}^{T}R_{xx}h}{{h^{T}R_{xx}h} + \sigma_{v}^{2}}.}$In addition to the square root, the other difference between thedecision statistics is in the numerator −ζ_(MECC) has the taps of the ECfilter ĥ^(T) in its numerator and ζ_(XMCC) has the true echo-pathimpulse response h^(T) in its numerator. However, for practicalimplementation and computational simplicity, ĥ^(T) can be substitutedfor h^(T) in ζ_(XMCC). Hence, the MECC (based on cross-correlation ofthe corrupted signal and the error signal) has the same performance asthe XMCC (based on cross-correlation of the reference signal and thecorrupted signal) but with an order of magnitude decrease incomputational complexity.

To determine the presence of near-end signal, the decision statistic canbe compared to a threshold and near-end signal can be declared if thedecision statistic is less than (or greater than depending on thedecision statistic used) the threshold. The threshold can be found, forinstance, by offline training in order to achieve some specifiedprobability of miss (P_(m)) (the probability of not detecting (miss)near-end signal when it is present).

Simulations were performed to assess the MECC. The performance wascharacterized in terms of the probability of miss (P_(m)) as a functionof near-end to far-end ratio (NFR) under a probability of false alarm(P_(f)) constraint. Since the probability of miss (P_(m)) is theprobability of not detecting (miss) near-end signal when it is present,a smaller value of P_(m) indicates better performance. To evaluate theMECC algorithm, the following steps were performed according to aprotocol described in Benesty, et al., “A New Class of DoubletalkDetectors Based on Cross-Correlation,” IEEE Transactions on Speech andAudio Processing, vol. 8, pp. 168-172, March 2000:

1. Set v=0 (No near-end speech).

-   -   (a) Select the threshold R_(Th) for the decision statistic;    -   (b) Compute P_(f);    -   (c) Repeat steps a, b over a range of threshold values; and    -   (d) Select the threshold that corresponds to P_(f)=0.1.

2. Select NFR value

-   -   (a) Select one of the four 2 second near-end speech samples;    -   (b) Select one of four positions within 5 second far-end speech;    -   (c) Compute P_(m);    -   (d) Repeat steps a, b, c over all sixteen conditions; and    -   (e) Average P_(m) over all sixteen conditions.

3. Repeat step 2 over a range of NFR values; and

4. Plot average P_(m) as a function of NFR.

Recorded digital speech sampled at 16 KHz was used as far-end signal xand near-end signal v and a measured L=8000 sample (500 ms) room impulseresponse of a 10′×10′×8′ room was used as the loudspeaker-microphoneenvironment h. The P_(m) characteristics of the MECC under theconstraint of P_(f)=0.1 were compared to those of the XECC as shown inFIG. 4. As shown, the MECC significantly outperformed the XECC over afull-range of NFR values.

To study the effects of echo-path variations, the decision statisticζ_(MECC) was plotted as a function of time (frames) in the absence ofnear-end speech as shown in FIGS. 5-6. The decision statistic wasgreater than threshold (ζ_(MECC)>R_(Th)) even after (300 frames)echo-path variations. This was observed for a variety of echo-pathvariations. However, if the echo-path change is large, the MECC can leadto false alarms by classifying echo in the presence of echo path changeas near-end signal. As will be explained in greater detail below, one ormore components that provide additional information can be added to theMECC to reduce the false alarm rate.

The MECC algorithm also can be implemented in the frequency domain byusing a frequency domain based decision statistic for each subband, forinstance:

${\xi_{MECC}( {k,t} )} = {1 - \frac{{\hat{r}}_{em}( {k,t} )}{{\hat{\sigma}}_{m}^{2}( {k,t} )}}$where {circumflex over (r)}_(em)(k,t) is an estimate of thecross-correlation coefficient between the corrupted signal and the errorsignal for the kth frequency subband and tth frame and {circumflex over(σ)}_(m) ²(k,t) is the estimate of the variance of the corrupted signalfor the kth frequency subband and tth frame. In one example, thecross-correlation coefficient between the corrupted signal and the errorsignal and the variance of the corrupted signal can be updated as:{circumflex over (r)} _(em)(k,t)=λ{circumflex over (r)}_(em)(k,t−1)+(1−λ)|E(k,t)M*(k,t)|{circumflex over (σ)}_(m) ²(k,t)=λ{circumflex over (σ)}_(m)²(k,t−1)+(1−λ)M(k,t)M*(k,t)where E(k,t) is the kth subband of the frequency domain transform of thetth frame of the echo cancellation error, M(k,t) is the kth subband ofthe frequency domain transform of the tth frame of the corrupted signalvector, and M*(k,t) is the conjugate of M(k,t). Any standard frequencydomain transform can be used such as the Fast Fourier Transform (FFT) orthe Modulated Complex Lapped Transform (MCLT). The estimate of thecorrupted signal variance (e.g., {circumflex over (σ)}_(m) ²(k,t))assumes that the corrupted signal has zero mean. If the corrupted signaldoes not have zero mean, the mean can also be recursively estimated andincorporated in the corrupted signal variance estimate. In anotherexample for the frequency domain, frame based decision statistic:{circumflex over (μ)}_(m)(k,t)=λ{circumflex over (μ)}_(m)(k,t−1)+M(k,t){circumflex over (σ)}_(m) ²(k,t)=λ{circumflex over (σ)}_(m)²(k,t−1)+(1−λ)(M(k,t)−{circumflex over (μ)}_(m)(k,t))(M(k,t)−{circumflexover (μ)}_(m)(k,t))*Where {circumflex over (μ)}_(m)(k,t) is the frame based estimate of themean of the corrupted signal and * designates the conjugate. Thedecision statistic can further include a noise term that is anindication of the noise in the kth frequency subband of the corruptedsignal, for instance, an estimate of the power of the noise. In oneexample, the subband decision statistic including the noise term can be:

${\xi_{MECC}( {k,t} )} = {1 - \frac{{{\hat{r}}_{em}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}{{\hat{\sigma}}_{m}^{2}( {k,t} )}}$where {circumflex over (σ)}_(z) ²(k,t) is the variance of the noise inthe kth subband and the tth frame. In another example, the subbanddecision statistic including the noise term can be:

${\xi_{MECC}( {k,t} )} = {1 - \frac{{{\hat{r}}_{em}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}{{{\hat{\sigma}}_{m}^{2}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}}$There are other methods of estimating the cross-correlation coefficientand the variance of the corrupted signal and any suitable method can beused.

FIG. 7 schematically illustrates one example of a system 700 forcontrolling an echo canceller (EC) having at least one adaptive filter710. The system 700 can include at least one adaptive filter controller720 corresponding to the adaptive filter 710 and configured to haltadaptation of the adaptive filter 710 according to the cross-correlationof the corrupted signal m and the error signal of the EC e, such as byemploying a decision statistic. As explained above, for AEC (shown inFIG. 7) the microphone signal is the corrupted signal and for LEC (notshown in FIG. 7) on a telecommunications device the signal input to thetelephone speaker is the corrupted signal. Any suitable decisionstatistic that is based on the cross-correlation of the corrupted signalm and the error of the EC e can be used such as those described aboveand below. The decision statistic can be implemented in the time domain(e.g.,

$( {{e.g.},{1 - \frac{{\hat{r}}_{em}(n)}{{\hat{\sigma}}_{m}^{2}(n)}},{1 - \frac{{\hat{r}}_{em}(t)}{{\hat{\sigma}}_{m}^{2}(t)}}} )$and can include a noise term (e.g.,

$( {{e.g.},{1 - \frac{{{\hat{r}}_{em}(n)} - {{\hat{\sigma}}_{z}^{2}(n)}}{{\hat{\sigma}}_{m}^{2}(n)}},{1 - \frac{{{\hat{r}}_{em}(n)} - {{\hat{\sigma}}_{z}^{2}(n)}}{{{\hat{\sigma}}_{m}^{2}(n)} - {{\hat{\sigma}}_{z}^{2}(n)}}},{1 - \frac{{{\hat{r}}_{em}(t)} - {{\hat{\sigma}}_{z}^{2}(t)}}{{\hat{\sigma}}_{m}^{2}(t)}},{1 - \frac{{{\hat{r}}_{em}(t)} - {{\hat{\sigma}}_{z}^{2}(t)}}{{{\hat{\sigma}}_{m}^{2}(t)} - {{\hat{\sigma}}_{z}^{2}(t)}}}} ).$The decision statistic can be used by the system 700 to halt theadaptation of the adaptive filter 710 in any suitable manner, forinstance when the decision statistic is less than a prescribedthreshold.

FIG. 8 schematically illustrates another example of a system 800 forcontrolling an acoustic echo canceller. The system 800 can have multipleadaptive filter controllers 810-830 and each adaptive filter controller810-830 can, for instance, correspond to a frequency subband. Each ofthe adaptive filter controllers 810-830 can be configured to halt itscorresponding adaptive filter 840-860 according to the cross-correlationof its corresponding corrupted signal and its corresponding error signalof its corresponding adaptive filter, for instance, by employing adecision statistic. One example of a frequency decision statistic thatcan be used is

$1 - \frac{{\hat{r}}_{em}( {k,t} )}{{\hat{\sigma}}_{m}^{2}( {k,t} )}$where {circumflex over (r)}_(em)(k,t) is an estimate of thecross-correlation coefficient between the corrupted signal and the errorsignal for the kth frequency subband and {circumflex over (σ)}_(m)²(k,t) is the estimate of the variance of the corrupted signal for thekth frequency subband and the tth frame. The decision statistic canfurther include a noise term that is an indication of the noise in thekth frequency subband, for instance, an estimate of the power of thenoise (e.g.,

$( {{e.g.},{1 - \frac{{{\hat{r}}_{em}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}{{\hat{\sigma}}_{m}^{2}( {k,t} )}},{1 - \frac{{{\hat{r}}_{em}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}{{{\hat{\sigma}}_{m}^{2}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}}} ).$

The adaptive filter controllers 810-830 can be configured to halt theircorresponding adaptive filters 840-860 according to the decisionstatistic, such as by halting the adaptive filters 840-860 when theirindividual decision statistics are less than a threshold. By way ofanother example, the decision statistic can be an overall decisionstatistic for controlling all subbands in the frame. The overalldecision statistic can be based on more than one of the individualdecision statistics (e.g., a few, some, most or all) corresponding tothe frequency subbands. For example, the overall decision statistic canbe based on the total number of individual decision statistics for eachfrequency subband that meet some criteria in reference to a threshold(e.g., are greater than, greater than or equal to, less than or lessthan or equal to a threshold). The total number can be, for instance,some, a few, about half, exactly half, most, or all. The adaptive filtercontrollers 810-830 described above can be implemented by software orcombinations of software and hardware and can be the same processexecuting on a single or a plurality of microprocessors or multipleprocesses executing on a single or a plurality of microprocessors.

FIG. 9 a flow diagram of one example of a method 900 of declaring that anear-end signal (e.g., speech) is present. At steps 910 and 920corrupted data and reference data are received. At step 930, thecorrupted data variance is estimated. At step 940, the EC output, orerror signal, is computed and at step 950 the corrupted signal and theEC output, or error signal, are cross-correlated. By way of example, inthe frequency domain, these steps can be performed at the subband level.A decision statistic is computed at step 960. Any suitable decisionstatistic (e.g., time domain based, frequency domain based) can be usedsuch as those described above and below. By way of example, in thefrequency domain, a decision statistic can be computed for eachfrequency subband. At steps 960 and 970, the decision statistic iscompared to a threshold value and if it is less than the threshold,near-end signal is declared in the capture data at 980. Any suitablethreshold can be used. For example, the thresholds can be found byoffline training in order to achieve some specified probability of miss(P_(m)). Although the steps 910-920 and 930-950 of the method 900 areillustrated in FIG. 9 as occurring in parallel, the steps can beperformed in any suitable order, including sequentially.

FIGS. 10-12 are flow diagrams of three examples of methods 1000, 1100,1200 of controlling an echo controller (e.g., AEC or LEC). At steps1010, 1110, 1210 and 1020, 1120, 1220 a corrupted signal and acancellation error from an adaptive filter of the echo canceller arereceived. Steps 1010, 1110, 1210 and 1020, 1120, 1220 can be performedin parallel or in any sequence. A decision statistic based on thecross-correlation of the corrupted signal and the cancellation error iscomputed as shown in FIG. 10 at step 1030. Any suitable decisionstatistic (e.g., time domain based, frequency domain based) can beemployed such as those described above and below. The decision statisticcan be based on an estimate of the cross-correlation of the corruptedsignal and the cancellation error (e.g., by obtaining an estimate usingan exponential recursive weighting algorithm) as shown in FIGS. 11 and12 at steps 1130 and 1230-1240. The decision statistic is compared to athreshold value at steps 1040, 1140, 1250 and a near-end signal (e.g.,speech) is declared present based on the comparison of the decisionstatistic to the threshold value at steps 1050, 1150, 1260. The near-endsignal can be declared present if, for instance, the decision statisticis less than to the threshold value. If using the estimatedcross-correlation decision statistic described below, the near-endsignal can be declared present if the decision statistic is greater thanthe threshold value.

The methods 1000, 1100, 1200 can be implemented in the frequency domain,for instance, by computing a plurality of decision statistics eachcorresponding to one of a plurality of frequency subbands at steps 1030,1130, 1240. Each of the plurality of decision statistics can be comparedto its threshold value at steps 1040, 1140, 1250 and the near-end signalcan be declared present based on the comparison at steps 1050, 1150,1260. The near-end signal can be declared present if, for instance, atleast some number (e.g., a few, some, most, about half, half or more,etc.) of the plurality of decision statistics are less than or less thanor equal to their threshold values. Any suitable thresholds can be used,such as those determined based on a probability of miss. As described inmore detail above, the decision statistic can be calculated at least inpart by employing an exponential recursive weighting algorithm.

FIG. 13 is a block diagram of one example of a system 1300 forfacilitating communication. The system includes means for canceling anecho in a corrupted signal 1310. The means for canceling the echo 1310(e.g., an acoustic echo canceller or a line echo canceller) includes oneor more adaptive filters 1320. The system 1300 also includes means forstopping 1330 the one or more adaptive filters from adapting based onthe cross-correlation of the corrupted signal with a residual signalproduced by the means for canceling the echo 1310. The means forstopping 1330 can be implemented in any suitable manner, such as thosedescribed above (e.g., time domain based decision statistic, frequencydomain based decision statistic, overall decision statistic). The meansdescribed above can be implemented by software or combinations ofsoftware and hardware and can be the same process executing on a singleor a plurality of microprocessors or multiple processes executing on asingle or a plurality of microprocessors.

By way of another example, a hybrid system can be configured by, forinstance, combining a cross-correlator (CC), a signal discriminator (SD)and optionally one or more detectors (e.g., corrupted signal detector(CSD) or a reference signal detector (RSD)) as shown in FIGS. 14-15 whenthe EC is an AEC. In the case of AEC, the corrupted signal is themicrophone signal and the reference signal is the speaker signal. In thecase of LEC (not shown) performed by a telecommunications device, thereference signal is the microphone signal and the corrupted signal isthe speaker signal. Such a hybrid system can be configured to not onlydetect near-end signal, but also to track echo path variationsefficiently as will be explained in more detail below. Any suitablecross-correlation measure can be utilized by the near-end speechdetector, such as one between the microphone (corrupted signal) and theAEC cancellation error or one between the communications device speaker(corrupted signal) and the LEC cancellation error.

The cross-correlation measure can either be computed in the time domainor the frequency domain. One example of a time domain, cross-correlationbased decision statistic that can be employed is:

${\xi_{MECC} = {1 - \frac{{\hat{r}}_{em}}{{\hat{\sigma}}_{m}^{2}}}},$which is based on either the sample estimates {circumflex over(r)}_(em)(n) and {circumflex over (σ)}_(m) ²(n) or the frame estimates{circumflex over (r)}_(em)(t) and {circumflex over (σ)}_(m) ²(t). Thesample estimates for time sample n can be found, for instance, by usingan exponential recursive weighting algorithm as:{circumflex over (r)} _(em)(n)=λ{circumflex over (r)}_(em)(n−1)+(1−λ)e(n)m(n){circumflex over (σ)}_(m) ²(n)=λ{circumflex over (σ)}_(m) ²(n−1)+(1−λ)m²(n)where e(n) is the echo cancellation error at time sample n, m(n) is thecaptured corrupted signal sample at the time sample n, and λ is anexponential weighting factor. In another example, the frame estimatesfor time frame t can also be found, for instance, by using anexponential recursive weighting algorithm, which is the maxima of thecorrelation in a frame:{circumflex over (r)} _(em)(t)=λ{circumflex over(r)}_(em)(t−1)+(1−λ)e(t)m ^(T)(t){circumflex over (σ)}_(m) ²(t)=λ{circumflex over (σ)}_(m)²(t−1)+(1−λ)m(t)m ^(T)(t)where e(t) is the echo cancellation error vector in the time frame t,m(t) is the captured corrupted signal vector at the time frame t, and λis an exponential weighting factor. As described previously, the meancan be recursively estimated and incorporated into both the sample-basedand frame-based corrupted signal variance estimates for corruptedsignals which do not have zero mean.

In addition to its simplicity, another advantage of the MECC decisionstatistic is that the maximum cross-correlation is computed instead ofcomputing the entire cross-correlation vector as is required by otheralgorithms (XMCC, XECC). This results in significant computationalsavings as compared to the other algorithms, requiring only 2multiplications, 2 additions, 1 subtraction and a division to computethe decision statistic at each sample (6 operations per sample), whereasfor the XMCC statistic 3L+3 operations are required to compute thedetection statistic at each sample where L is the frame size (typicallyL≧512). The performance of the MECC in comparison to the XMCC is shownin FIG. 16.

An example of a frequency domain, cross-correlation based decisionstatistic that can be employed is:

${\xi_{MECC}( {k,t} )} = {1 - \frac{{\hat{r}}_{em}( {k,t} )}{{\hat{\sigma}}_{m}^{2}( {k,t} )}}$where {circumflex over (r)}_(em)(k,t) is an estimate of thecross-correlation coefficient between the corrupted signal and the errorsignal for the kth frequency subband and tth frame and {circumflex over(σ)}_(m) ²(k,t) is the estimate of the variance of the corrupted signalfor the kth frequency subband and the tth frame. In one example, thecross-correlation coefficient between the corrupted signal and the errorsignal and the variance of the corrupted signal can be updated as:{circumflex over (r)} _(em)(k,t)=λ{circumflex over (r)}_(em)(k,t−1)+(1−λ)|E(k,t)M*(k,t)|{circumflex over (σ)}_(m) ²(k,t)=λ{circumflex over (σ)}_(m)²(k,t−1)+(1−λ)M(k,t)M*(k,t)where E(k,t) is the kth subband of the frequency domain transform of thetth frame of the echo cancellation error, M(k,t) is the kth subband ofthe frequency domain transform of the tth frame of the corrupted signalvariance, and * designates the conjugate. As described previously, themean can be recursively estimated and incorporated into both thefrequency domain corrupted variance estimates for corrupted signalswhich do not have zero mean. The decision statistic can further includea noise term that is an indication of the noise in the kth frequencysubband, for instance, an estimate of the power of the noise. In oneexample, the subband decision statistic including the noise term can be:

${\xi_{MECC}( {k,t} )} = {1 - \frac{{{\hat{r}}_{em}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}{{\hat{\sigma}}_{m}^{2}( {k,t} )}}$where {circumflex over (σ)}_(z) ²(k,t) is the variance of the noise inthe kth subband and tth frame. In another example, the subband decisionstatistic including the noise term can be:

${\xi_{MECC}( {k,t} )} = {1 - {\frac{{{\hat{r}}_{em}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}{{{\hat{\sigma}}_{m}^{2}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}.}}$

By way of another example, a time domain, estimated cross-correlationdecision statistic which is updated using an exponential recursiveweighting algorithm can be used as the cross-correlation measure asfollows:P _(e) ²(n)=λP _(e) ²(n−1)+(1−λ)e(n)e(n)P _(m) ²(n)=λP _(m) ²(n−1)+(1−λ)m (n)m(n)P _(m,e)(n)=λP _(m,e)(n−1)+(1+λ)e(n)m(n)where P_(m,e)(n) is the cross-correlation of the corrupted signal andthe error signal, P_(e)(n) is the standard deviation of the echocanceller's error signal, P_(m)(n) is the standard deviation of thecorrupted signal, e(n) is the captured cancellation error in the timesample n and m (n) is the captured corrupted signal at the time sample nand λ is the exponential weighting factor. Since smaller values of λprovide better tracking capability but worse estimation accuracy, forslowly time varying signals 0.9≦λ≦1 can be chosen. This estimatedcross-correlation decision statistic (ECC) can be given by:

${{ecc}(n)} = {\frac{P_{m,e}(n)}{{P_{e}(n)}{P_{m}(n)}}.}$

By way of another example, a time domain, estimated cross-correlationdecision statistic which indicates the maxima of the correlation in aframe and is updated using an exponential recursive weighting algorithmcan be used as the cross-correlation measure as follows:P _(e) ²(t)=λP _(e) ²(t−1)+(1−λ)e(t)e ^(T)(t)P _(m) ²(t)=λP _(m) ²(t−1)+(1−λ)_(m)(t)m ^(T)(t)P _(m,e)(t)=λP _(m,e)(t−1)+(1−λ)e(t)_(m) ^(T)(t)where P_(m,e)(t) is the cross-correlation of the corrupted signal andthe error signal, P_(e)(t) is the standard deviation of the echocanceller's error signal, P_(m)(t) is the standard deviation of thecorrupted signal, e(t) is the captured cancellation error vector in thetime frame t and m(t) is the captured corrupted signal vector at thetime frame t and λ is the exponential weighting factor. Since smallervalues of λ provide better tracking capability but worse estimationaccuracy, for slowly time varying signals 0.9≦λ≦1 can be chosen. Thisestimated cross-correlation decision statistic (ECC) can be given by:

${{ecc}(t)} = {\frac{P_{m,e}(t)}{{P_{e}(t)}{P_{m}(t)}}.}$

By way of another example, a frequency domain, estimatedcross-correlation decision statistic which indicates the maxima of thecorrelation in a frame and is updated using an exponential recursiveweighting algorithm can be used as the cross-correlation measure asfollows:P _(m) ²(k,t)=λP _(e) ²(k,t−1)+(1−λ)E(k,t)E*(k,t)P _(m) ²(k,t)=λP _(m) ²(k,t−1)+(1−λ)M(k,t)M*(k,t)P _(m,e)(k,t)=λP _(m,e)(k,t−1)+(1−λ)|E(k,t)M*(k,t)|where P_(m,e)(k,t) is the cross-correlation of the corrupted signal andthe error signal, P_(e)(k,t) is the standard deviation of the echocanceller's error signal, P_(m)(k,t) is the standard deviation of thecorrupted signal, E(k,t) is the kth subband of the frequency domaintransform of the tth frame of the echo cancellation error, M(k,t) is thekth subband of the frequency domain transform of the tth frame of thecorrupted signal vector, and * designates the conjugate. Any standardfrequency domain transform can be used such as the Fast FourierTransform (FFT) or the Modulated Complex Lapped Transform (MCLT). Sincesmaller values of λ provide better tracking capability but worseestimation accuracy, for slowly time varying signals 0.9≦λ≦1 can bechosen. The estimated cross-correlation decision statistic (ECC) can begiven by:

${{ecc}( {k,t} )} = {\frac{P_{m,e}( {k,t} )}{{P_{e}( {k,t} )}{P_{m}( {k,t} )}}.}$

When using these ECC decision statistic as the cross-correlationmeasure, cross-correlation is high whenever there is a change in theecho-path and/or when the near-end signal is present. Thus, the ECC canbe compared to a threshold and when they are greater than the threshold,near-end signal and/or echo-path change can be considered present. Whenusing the MECC decision statistic as the cross-correlation measure, thedecision statistic is low (e.g., lower than a threshold value) wheneverthere is a change in the echo-path and/or when the near-end signal ispresent. Any suitable thresholds can be used, such as those determinedaccording to the probability of miss.

Both the MECC and the ECC can be used for EC algorithms. For the case ofLEC performed at the communications device (e.g., telephone), thecorrupted signal is the communications device speaker, the echo is themicrophone echo due to the hybrid and the reference signal is themicrophone signal. The near-end signal is the speech that is transmittedto the speaker through the telephone line. The corrupted signal cancontain near-end signal, microphone echo and noise. Echo-path change inthe LEC case is due to changes in the impedance of the equipment.

In order to differentiate near-end signal from echo-path variations,detectors and discriminators can be used. By way of example, a signaldiscriminator (SD) and optionally one or more signal detectors (e.g.,corrupted signal detector (CSD) or reference signal detector (RSD)) canbe used to detect the presence of near-end signal as shown in FIGS.14-15C. Although FIGS. 14-15C show a speaker and a microphone, the sameprinciples that are discussed in reference to these figures apply to LECas well. The detector/discriminator can be, for instance, a frequencydomain logistic discriminative detector, such as a real time recurrentlearning (RTRL) network that employs, for instance, stochastic gradientdescent to train the network in order to minimize the cross-entropyerror. Using such an error metric makes the network discriminative andprovides the maximum likelihood estimate of the class probability for awide variety of class conditional densities of the data.

Since the outputs represent probabilities, this facilitates decisionmaking as well as the combination of decisions. The class probabilitycan be estimated as:

$P_{t} = \frac{1}{1 + {\exp( {{- W^{T}}\chi_{t}} )}}$where P_(t) is the probability of speech at time frame t, W^(T) aretrained weights (1× frequency bins) and χ_(t) is a vector of extractedfeatures in each frequency bin at the time frame t. The trained weightsW^(T) can be obtained offline using real time recurrent learning (RTRL)techniques.

It is desirable for a detector's features to be simple and easy tocalculate, to have discriminatory power and to work well under changingnoise conditions. For instance, the estimated posterior signal-to-noiseratio (SNR) χ(k,t) can be used as the feature set for the corruptedsignal detector (CSD). The estimated posterior SNR χ(k,t) is the ratioof the energy in a given target time-frequency atom A to the noiseenergy N

${\chi( {k,t} )} = \frac{{{A( {k,t} )}}^{2}}{N( {k,t} )}$where k,t are the frequency bin and time indices respectively. For thecorrupted signal detector (CSD), the logarithm of the estimatedposterior SNR of the corrupted signal can be used as the feature:χ_(CSD)(k,t)={log |M(k,t)|²−log N _(M)(k,t)}where N_(M)(k,t) is the noise energy in frequency bin k and time-frame tof the corrupted signal, and M(k,t) is the kth bin of the frequencydomain transform of the corrupted signal in time frame t. The noiseenergy N_(M) can be tracked using any suitable algorithm such as bytracking the noise floor using a minima tracker (looking back a fewframes (e.g., 25) for each frequency bin and choosing the lowest valueof the signal) followed by smoothing. Examples of noise-trackingalgorithms are described in Israel Cohen and Baruch Berdugo, “SpeechEnhancement for Non-stationary Noise Environments,” Signal Processing,vol. 81, pp 2403-2418 (2001) and R. Martin, “Spectral Subtraction Basedon Minimum Statistics,” Proceedings of the 7th European SignalProcessing Conference, September 1994, pp. 1182-1185. In anotherexample, the CSD can discriminate between noise and any signal such asnear-end signal, echo, or doubletalk using an energy based teststatistic such as:σ_(m) ²>Twhere σ_(m) ² is the variance of the corrupted signal and T is athreshold selected by offline training to achieve a specifiedprobability of miss for a given false alarm rate.

The CSD detector indicates the presence of signal (as opposed to noise)at the microphone for AEC, which can be due to near-end signal (e.g.,speech) or due to loudspeaker echo. The CSD detector indicates thepresence of signal (as opposed to noise) at the speaker for LECperformed at the telephone, which can be due to near-end signal (e.g.,speech) or due to microphone echo. To differentiate the near-end signalfrom the echo, a discriminator (SD) can be used. Any suitablediscriminator can be used. For instance, the signal discriminator (SD)can be designed to determine how much of the corrupted signal isdominated by the near-end signal as opposed to the echo. To distinguishthe near-end signal from the echo, features, such as the logarithm ofthe corrupted signal instantaneous power and the reference signalinstantaneous power, can be used:χ_(SD)(k,t)={log |M(k,t)|²−log |X(k,t)|²}where M(k,t) is the kth bin of the frequency domain transform of thecorrupted signal in time frame t and X is the kth bin of the frequencydomain transform of the reference signal in time frame t.

For the SD described above, the extracted features are typically largestfor the near-end signal only case, smallest for the echo only case(i.e., echo without near-end signal), and in between for the case ofdoubletalk. Different feature levels correspond to different probabilitylevels with larger features corresponding to higher probabilities. Forthe echo only case, testing showed that the extracted features were lowindependent of the echo-path; hence the discriminator is independent ofthe echo-path in the echo only case. Thus, since the SD features areessentially the corrupted signal subband power divided by thecorresponding reference signal subband power and the echo is often smallfor any echo path, the SD features will not change much during an echopath change. However, this depends on the near-end to far-end ratio andthe echo-path gain.

Since the MECC and the ECC decision statistics described above alonecannot distinguish near-end signal from echo only in the presence ofecho-path change, the SD, the CSD, and/or the RSD can be used todistinguish the presence of near-end signal from the presence of echoonly with echo-path change. By way of example, the CC shown in FIG. 14can be implemented using the ECC decision statistic described above, andthe system can control the adaptive filter(s) as follows:

-   -   1) ECC=high+SD=high+CSD=high: near-end signal is considered        present and the filter(s) are prevented from adapting;    -   2) ECC=high+SD=low+CSD=high: echo only with echo-path change is        considered present and the filter(s) are allowed to adapt;    -   3) ECC=low+SD=low+CSD=low: both near-end signal and echo are        considered absent and the filter(s) are prevented from adapting;        and    -   4) ECC=low+SD=low+CSD=high: echo only without echo-path change        is considered present and the filter(s) are allowed to adapt.        The words “present” and “absent” are used herein to mean some        likelihood or probability of the presence or the absence of a        signal or echo-path change. Likewise, the terms “no” and        “without” in reference to the signals are used to indicate a        likelihood or a probability of the absence of a signal or echo.        A similar system can be configured using the MECC decision        statistic instead of the ECC, although the MECC will be high        when the ECC is low and vice versa. With regard to condition 3        above, the CSD alone provides enough information to make this        decision since the CSD is low whenever there is no near-end        signal and no echo in the corrupted signal. Thus, the decision        to allow the filters to continue to adapt because there is only        noise can be based solely on the CSD. With regard to condition        2, when echo only and echo-path change are present, the        information can be used to further improve the system, such as        by accelerating the adaptation of the filters or switching        between different EC algorithms (for instance, between a slower,        more accurate algorithm and a faster less accurate algorithm).

The CC and the SD can be implemented in any suitable manner, such as byusing any suitable decision statistic and an RTRL network as describedabove. If the ECC decision statistic is used, when the ECC is greaterthan its threshold and the probability of the SD is low and that of theCSD is high, echo only with echo-path change can be declared. Thus, thehybrid detector can track echo-path changes. If the MECC is used insteadof the ECC, it will be low, the SD will be low and the CSD will be highin the presence of echo only with echo-path change.

By way of another example, a reference signal detector (RSD) can be usedin place of the CSD as shown in FIG. 15B. Any suitable RSD can be used.For instance, the RSD can use the reference signal X as the targetsignal and the logarithm of the SNR estimate rather than the SNRestimate itself can be used as the input to a RTRL network:χ_(RSD)(k,t)={log |X(k,t)|²−log N _(X)(k,t)}where N_(X) is the noise energies in frequency bin k and time-frame t ofthe reference signal. The noise power N can be tracked using anysuitable algorithm such as those described above. In another example,the RSD can discriminate between noise and any reference signal using aenergy based test statistic such as:σ_(x) ²>Twhere σ_(x) ² is the variance of the reference signal and T is athreshold select by offline training to achieve a specified probabilityof miss for a given false alarm rate. A system having the ECC as the CCcan, for instance, control the adaptive filter(s) according to thefollowing conditions:

-   -   1) ECC=high+SD=high+RSD=high: most likely near-end signal and        echo present, although the echo could be very weak. The        filter(s) can be prevented from adapting during this condition;    -   2) ECC=high+SD=high+RSD=low: near-end signal only is considered        present. The filter(s) can be prevented from adapting;    -   3) ECC=high+SD=low+RSD=high: echo with echo-path change is        considered present and near-end signal is considered absent. The        filter(s) can be allowed to adapt;    -   4) SD=low+RSD=low: both near-end signal and echo are considered        absent (only noise present). The filter(s) can be prevented from        adapting; and    -   5) ECC=low+SD=low+RSD=high: echo without echo-path change is        considered present and near-end signal is considered absent. The        filter(s) can be allowed to adapt.        A similar system can be configured using the MECC decision        statistic instead of the ECC, although the MECC will be high        when the ECC is low and vice versa.

By way of yet another example, a reference signal detector (RSD) can beused along with the CSD, SD and CC as shown in FIG. 15C. Any suitableCSD, RSD, SD and CC can be used such as those described above. Moreover,a second cross-correlation measure can be used as an SD in order todiscriminate near-end signal from echo in the corrupted signal.

By way of another example, a learner that combines multiple decisions(e.g., CSD and SD or RSD and SD or CSD, RSD and SD) into one can be usedin the systems described above. For example, a RTRL network can betrained to indicate the presence of a near-end signal when the inputfeatures for the network are the outputs of the CSD and SD networks. TheCSD, SD and CC can be configured in any suitable way, such as the waysdescribed above. For instance, the CC can be implemented using ecc(k,t)or ζ_(MECC)(k,t) in comparison to a threshold and the CSD and SD can beimplemented to compute a probability and the probabilities can becompared to a threshold. The threshold for the SD can be selected, forinstance, based on the probability of miss of the near-end signal. Byway of example, the SD threshold can be selected so that the probabilityof miss of the near-end signal is some specified level. The thresholdfor the CSD can be selected using offline training to discriminate noiseonly from other conditions (e.g., near-end signal, echo only,doubletalk). By way of another example, the SD and CC thresholds can bejointly selected in order to achieve an overall probability of miss forthe near-end signal. For instance, test all combinations of thresholdvalues for both the SD and the CC ranging from 0 to 1 and choose thecombination of thresholds that yield the lowest probability of miss fora given false alarm constraint. Similarly, thresholds for the threecomponents (e.g., CC, SD and CSD) or four (e.g., CC, SD, CSD and RSD)components can be selected together.

The performance of the hybrid detector shown in FIG. 14 to detectnear-end signal is shown in FIG. 17. To assess the performance,simulations were performed and performance characterized in terms of theprobability of miss (P_(m)) as a function of near-end to far-end ratio(NFR) under a probability of false alarm (P_(f)) constraint. Theprobability of miss (P_(m)) is the probability of not detecting (miss)double-talk when it is present. Thus, a smaller value of P_(m) indicatesbetter performance.

Recorded digital speech sampled at 16 KHz was used as reference signal xand near-end signal v and a measured L=8000 sample (500 ms) room impulseresponse of a 10′×10′×8′ room was used as the loudspeaker-microphoneenvironment h. The hybrid detector was compared to a conventionalcross-correlation (XECC) based double-talk detector as well as to anRTRL based double-talk detector (described in U.S. patent applicationSer. No. 11/669,549).

As can be seen in FIG. 17, the hybrid and the MECC algorithmssignificantly outperform the XECC over a full-range of NFR values. Ascan also be seen from the results, the performance of the MECC issimilar to that of the XMCC. However, as explained above, the MECC iscomputationally very efficient, is insensitive to echo-path variationsand the detection threshold is independent of the data. Echo-pathchanges were not included in this testing. If echo-path changes had beenincluded in the testing, the hybrid detector would allow the EC to adaptwhen the echo-path changes occurred. It is desirable for the EC to adaptduring periods of echo-path changes in the absence of near-end signal.

FIG. 18 is a block diagram of one example of a system 1800 forcontrolling an echo canceller (EC) 1810. The system 1800 includes acorrupted signal detector (CSD) 1820, a signal discriminator (SD) 1830,a cross-correlator (CC) 1840 and a halter 1850. The CSD 1820 isconfigured to detect the presence of a signal other than noise in thecorrupted signal 1860. The SD 1830 is configured to discriminate betweennear-end signal and reference signal echo 1870 in the corrupted signal1860. The CC 1840 is configured to cross-correlate the corrupted signal1860 and the error signal 1880 of the echo canceller 1810. The halter1850 is configured to halt adaptation of the EC 1810 if the CSD 1820indicates noise only in the corrupted signal 1860 or the presence ofsignal other than noise in the corrupted signal 1860 and the SD 1820 andthe CC 1840 indicate the presence of near-end signal in the corruptedsignal 1860. The system 1800 can be implemented in any suitable manner,such as any of the ways described above. For instance the CC 1840 can beconfigured to employ a decision statistic (e.g., time domain decisionstatistic, frequency domain decision statistic, overall decisionstatistic) and the CSD 1820 and the SD 1830 can be configured to employreal time recurrent learning networks to detect and discriminate. TheCSD also can be configured to discriminate noise from signal includingnear-end signal, echo, and doubletalk using an energy based teststatistic. The CSD, SD and CC described above can be implemented bysoftware or combinations of software and hardware and can be the sameprocess executing on a single or a plurality of microprocessors ormultiple processes executing on a single or a plurality ofmicroprocessors.

FIG. 19 is a block diagram of another example of a system 1900 forcontrolling an echo canceller (EC) 1910. The system 1900 includes across-correlator 1920, a signal discriminator 1930, one or moredetectors 1940 and a controller 1950. The cross-correlator 1920 isconfigured to utilize cross-correlation to produce an output indicativeof the presence of near-end signal or the presence of echo only (nonear-end signal) with echo-path change in a corrupted signal. Near-endsignal includes but is not limited to speech. In the case of AEC,echo-path change indicates a variation of the acoustic path from thespeaker to the microphone in the near-end room. The signal discriminator1930 is configured to produce an output that distinguishes between thepresence of near-end signal and the presence of echo in the corruptedsignal. The one or more detectors 1940 are configured to produce anoutput that detect any signal in either the corrupted signal or thereference signal other than noise. The one or more detectors 1940 can beimplemented by a plurality of detectors such as the CSD and the RSDdescribed above. The cross-correlator 1920, signal discriminator 1930and the detector(s) 1940 described above can be implemented by softwareor combinations of software and hardware and can be the same processexecuting on a single or a plurality of microprocessors or multipleprocesses executing on a single or a plurality of microprocessors.

The controller 1950 is configured to control adaptation of the echocanceller 1910 according to the outputs of the cross-correlator 1920,signal discriminator 1930 and the one or more detectors 1940. The system1900 can be implemented in any suitable manner, such as in any of theways described above. By way of example, the cross-correlator 1920 canbe implemented as a time domain or a frequency domain cross-correlator,for instance by employing a decision statistic. Two examples of suitabledecision statistics are ecc (k,t) and ζ_(MECC)(k,t) described above. Thedecision statistic can be based at least in part on a noise signal, canbe based on estimates and/or can be an overall decision statistic. Byway of yet another example, the signal discriminator 1930 and the one ormore detectors 1940 can be real time recurrent learning networks orenergy based.

FIG. 20 is a flow diagram of one example of a method 2000 of declaring anear-end signal present in a microphone signal. At steps 2010 and 2020,corrupted data and reference data are received. The probability of asignal being in the corrupted data (as opposed to only noise) iscomputed at step 2030 and at step 2040, the corrupted signal variance isestimated. At step 2050, the probability that a near-end signal iscontained in the corrupted data is computed using real time recurrentlearning (RTRL) techniques. The EC output, or error signal, is computedat step 2060 and at step 2070, cross-correlation of the corrupted signaland the EC output, or error signal, is computed. The decision statisticis computed at step 2080 and near-end signal is declared according tothe decision statistic and the probabilities at step 2090. Steps 2010and 2020 can occur in parallel or in any sequence as can steps2030-2060. The method 2000 can be implemented using an energy baseddecision statistic rather than a RTRL probability.

FIG. 21 is a flow diagram of another example of a method 2100 ofcontrolling an echo canceller. At step 2110, a cross-correlation basedoutput is produced and at step 2120, a discrimination output isproduced. Steps 2110 and 2120 can be performed in any sequential orderor in parallel. At step 2130, the echo canceller is controlled accordingto the cross-correlation based output and the discrimination output. Themethod 2100 can optionally include the step 2140 of producing one ormore detector outputs.

The steps of the method 2100 can be performed in any suitable mannersuch as in any of the ways described above or below. By way of example,step 2110 can be performed in either the time domain or the frequencydomain. The cross-correlation based output can be an estimatedcross-correlation output and can be, for instance, produced by computinga decision statistic. By way of another example, the discriminationoutput and detector output(s) produced can be probabilities and can bebased at least in part on machine learning (e.g., produced by one ormore real time recurrent learning networks). By way of yet anotherexample, step 2130 can be accomplished by comparing a decision statisticto its corresponding threshold and comparing the one or moreprobabilities to their corresponding thresholds and halting the echocanceller according to the results of the comparison.

FIG. 22 is another example of a system 2200 for controlling an echocanceller (EC) 2210. The system 2200 includes a means for determining2220 whether a near-end signal is present in a corrupted signal, a meansfor cross-correlating 2230 two signals, and a means for stopping 2240one or more adaptive filters 2250 of the echo canceller 2210 fromadapting based on the presence of near-end signal and thecross-correlation. The system 2200 optionally can include a means fordetecting 2260. The EC 2210, means for determining 2220, means forcross-correlating 2230 and means for stopping 2240 can be implemented inany suitable manner, such as in any of the ways described above orbelow. By way of example, the means for cross-correlating 2230 can beimplemented using an ECC, MECC, XMCC, or XECC algorithm and can beimplemented in either the time domain or the frequency domain, such asby using a decision statistic. The decision statistic can be anindividual or an overall decision statistic. In the case of the former,each of the one or more adaptive filters 2250 can be stopped accordingto an individual decision statistic (e.g., at a frequency bin level). Inthe case of the latter, the overall decision statistic can reflect somenumber of the individual decision statistics (e.g., all or some or mostor a few) and all of the adaptive filters can be stopped according tothe overall decision statistic. One example of an overall decisionstatistic is configured to stop the one or more adaptive filters whenhalf or more of the individual decision statistics meet some criteria,for instance, when half or more of the individual statistics exceedtheir prescribed thresholds.

By way of another example, the means for discriminating 2220 can beimplemented by using a signal discriminator (SD) and the means fordetecting can be implemented by a corrupted signal detector (CSD) asdescribed above. By way of yet another example, the means for stopping2240 can be implemented using prescribed thresholds as described above.The means described above can be implemented by software or combinationsof software and hardware and can be the same process executing on asingle or a plurality of microprocessors or multiple processes executingon a single or a plurality of microprocessors.

FIGS. 23-29 are block diagrams of examples of various configurations ofsystems for controlling an echo canceller (EC) having associated signals(e.g., a corrupted signal, a reference signal and an error signal). Thesystems include a signal indicator and an echo canceller controller. Thesignal indicator can be configured to indicate at least one or more ofthe following conditions in the corrupted signal based at least in parton the cross-correlation of two of the signals associated with the EC(e.g., the corrupted signal and the error signal):

1) near-end signal;

2) echo only with echo-path change;

3) echo only without echo-path change; and/or

4) noise only (no near-end signal and no echo).

Additionally or alternatively, the signal indicator also can beconfigured to detect noise only in the reference signal.

The signal indicator can include, for instance, one or morecross-correlators (CC), one or more signal discriminators (SD) andoptionally one or more detectors (D). The CC(s) can be configured toproduce an indication based on cross-correlating two of the signalsassociated with the EC. The SD(s) can be configured to produce anindication of whether near-end signal or echo is present in thecorrupted signal. The D(s) can be configured to produce an indication ofwhether noise only is present in the corrupted signal or in thereference signal. The cross-correlators, the discriminators and thedetectors can be implemented in any suitable manner, such as at afrequency bin level or the CCs can be configured at the frequency binlevel and the SD and D(s) can be implemented at the frame level. Thesignal indicator can be configured entirely in the time domain and haveone CC, one SD and one D. The signal indicator can be configured to haveat least two CCs that do not cross-correlate the same two signalsassociated with the EC (e.g., each CC cross-correlates at least onedifferent signal associated with the EC, for instance, one CCcross-correlates the corrupted signal and the error signal and anotherCC cross-correlates the reference signal and the error signal).

The EC Controller can be configured to control the EC according to theindications of the signal indicator. By way of example, the ECController can employ decision statistics (e.g., individual or overall)to control the EC. By way of example, if the indications from the signalindicator indicate that the current period of the corrupted signal is aperiod of near-end signal, the EC Controller can, for instance, preventat least one of the EC's one or more adaptive filters from adapting. Ifthe indications from the signal indicator indicate that the currentperiod of the corrupted signal is a period of echo only with echo-pathchange, the EC Controller can, for instance, allow the at least one ofthe EC's one or more adaptive filters to adapt, accelerate adaptation orswitch to another echo canceller algorithm. If the indications indicatea period of no near-end signal and no echo (noise only), the ECController can, for instance, prevent at least one of the EC's adaptivefilters from adapting. If the indications indicate a period of echo onlywithout echo-path change, the EC Controller can, for instance, allow atleast one of the echo canceller's one or more adaptive filters to adapt.

As shown in FIG. 24, one example of such a system 2400 can control aplurality of adaptive filters 2410 individually, for instance, if the ECcontroller 2420 is implemented at the frequency bin level (e.g., byreceiving input from a signal indicator 2430 that has a plurality ofcomponents 2440 implemented at the frequency bin level). As shown inFIG. 25, the components 2540 of the signal indicator 2530 can include across-correlator (CC), a signal discriminator (SD) and optionally one ormore detectors (D). As shown in FIGS. 26, 27 and 29, the EC controller2620, 2720, 2920 can control all of the adaptive filters 2610, 2710,2910 in the same manner (e.g., by using an overall decision statistic).As shown in FIG. 28, the signal indicator 2830 can include a pluralityof cross correlators (CC), a single signal discriminator (SD) andoptionally one or more detectors (D) (e.g., the cross-correlators beingimplemented at the frequency bin level and both the signal discriminatorand the one or more detectors being implemented at the frame level). TheCC, SD and the D(s) described above can be implemented by software orcombinations of software and hardware and can be the same processexecuting on a single or a plurality of microprocessors or multipleprocesses executing on a single or a plurality of microprocessors.

FIG. 30 is a flow chart that illustrates one example of a method 3000 ofcontrolling an echo canceller (EC). Such a method 3000 can beimplemented by computer-executable instructions stored on one or morecomputer-readable media. At step 3010, a cross-correlation based output(CC) that indicates the presence of near-end signal (NES), the presenceof echo only with echo-path change (EPC) or the presence of noise only(NO) in a corrupted signal is produced. At step 3020, a discriminationoutput (SD) that differentiates the presence of near-end signal (NES)from the presence of echo (ECHO) in the corrupted signal is produced.The echo canceller is controlled according to the cross-correlationbased output and the discrimination output at step 3030.

The steps of the method 3000 can be performed in any suitable order, forinstance, steps 3010 and 3020 can be performed in parallel or insequence. The steps of the method 3000 can be performed in any suitablemanner such as in the ways described above or below. For instance, thecross-correlation based output can be produced by computing a decisionstatistic such as the MECC or ECC decision statistics described aboveand the discrimination output can be produced using a real timerecurrent learning network to yield a probability. The echo cancellercan be controlled, for example, by comparing the decision statistic andthe probability to their threshold and by using that information to makea decision regarding whether or not to prevent or allow the EC to adapt.

FIG. 31 is a block diagram of a system 3100 for controlling an echocanceller. The system 3100 can include two or more cross-correlators3120 and an echo canceller controller 3130. The two or morecross-correlators 3120 each can be configured to detect one or moreconditions in a corrupted signal based on a cross-correlation of twosignals (e.g., the corrupted signal, the reference signal or the errorsignal) associated with the echo canceller. Any suitable combination ofcross-correlators can be used in the system, such as the XMCC incombination with either the XECC, the MECC or the ECC. The echocanceller controller can be configured to control the echo cancellerbased on the conditions detected in the corrupted signal by the two ormore cross-correlators including but not limited to detecting thepresence of near-end signal, detecting the presence of echo only withecho-path change, detecting the presence of echo only without echo-pathchange, and detecting the presence of doubletalk.

FIG. 32 is a block diagram of a system 3200 for controlling an echocanceller. The echo canceller 3210 is configured to receive a corruptedsignal and a reference signal and to produce an error signal. The system3200 includes means for producing indicators 3220 and means forcontrolling the echo canceller based on the indicators 3230. Theindicators can include an echo only in the presence of echo-path changeindicator, a near-end signal indicator, an echo only in the absence ofecho-path change indicator and an absence of near-end signal in theabsence of echo indicator. The echo only in the presence of echo-pathchange indicator can be produced based at least in part oncross-correlating two signals associated with the echo canceller 3210 inany suitable manner, such as the ways described above.

FIG. 33 is a flow chart of a method 3300 of controlling an echocanceller. Such a method 3300 can be implemented by computer-executableinstructions stored on one or more computer-readable media. At step3310, two signals associated with the echo canceller (e.g., corruptedsignal, reference signal, error signal) are cross-correlated to producea cross-correlation result. At step 3320, if the presence of near-endsignal in the corrupted signal is detected, adaptation of at least oneof the echo canceller's one or more adaptive filters is stopped at step3330. At step 3320, if the presence of echo-path change in the corruptedsignal is detected in the absence of near-end signal, adaptation of atleast one of the echo canceller's one or more adaptive filters iscontinued (e.g., continued at the same speed or accelerated) at step3330. At step 3320, the method can further include detecting the absenceof near-end signal and the absence of echo in the corrupted signaland/or detecting the presence of echo only without echo path change. Ifsuch conditions are detected, at step 3330, adaptation is halted in theformer case or continued in the latter case.

The systems described above can be implemented in whole or in part byelectromagnetic signals. These manufactured signals can be of anysuitable type and can be conveyed on any type of network. For instance,the systems can be implemented by electronic signals propagating onelectronic networks, such as the Internet. Wireless communicationstechniques and infrastructures also can be utilized to implement thesystems.

The methods can be implemented by computer-executable instructionsstored on one or more computer-readable media or conveyed by a signal ofany suitable type. The methods can be implemented at least in partmanually. The steps of the methods can be implemented by software orcombinations of software and hardware and in any of the ways describedabove. The computer-executable instructions can be the same processexecuting on a single or a plurality of microprocessors or multipleprocesses executing on a single or a plurality of microprocessors. Themethods can be repeated any number of times as needed and the steps ofthe methods can be performed in any suitable order.

The subject matter described herein can operate in the general contextof computer-executable instructions, such as program modules, executedby one or more components. Generally, program modules include routines,programs, objects, data structures, etc., that perform particular tasksor implement particular abstract data types. Typically, thefunctionality of the program modules can be combined or distributed asdesired. Although the description above relates generally tocomputer-executable instructions of a computer program that runs on acomputer and/or computers, the user interfaces, methods and systems alsocan be implemented in combination with other program modules. Generally,program modules include routines, programs, components, data structures,etc. that perform particular tasks and/or implement particular abstractdata types.

Moreover, the subject matter described herein can be practiced with allcomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, personal computers, stand-alone computers, hand-heldcomputing devices, wearable computing devices, microprocessor-based orprogrammable consumer electronics, and the like as well as distributedcomputing environments in which tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote memory storage devices. The methods and systemsdescribed herein can be embodied on a computer-readable medium havingcomputer-executable instructions as well as signals (e.g., electronicsignals) manufactured to transmit such information, for instance, on anetwork.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing some of the claims.

It is, of course, not possible to describe every conceivable combinationof components or methodologies that fall within the claimed subjectmatter, and many further combinations and permutations of the subjectmatter are possible. While a particular feature may have been disclosedwith respect to only one of several implementations, such feature can becombined with one or more other features of the other implementations ofthe subject matter as may be desired and advantageous for any given orparticular application.

In regard to the various functions performed by the above describedcomponents, computer-executable instructions, means, systems and thelike, the terms are intended to correspond, unless otherwise indicated,to any functional equivalents even though the functional equivalents arenot structurally equivalent to the disclosed structures. To the extentthat the terms “includes,” and “including” and variants thereof are usedin either the specification or the claims, these terms are intended tobe inclusive in a manner the same as the term “comprising.” Furthermore,any use of the conjunctions “or” and “and” are intended to benon-exclusive. Accordingly, the claimed subject matter is intended toembrace all such alterations, modifications, and variations that fallwithin the spirit and scope of the appended claims.

1. A system comprising: an echo canceller having one or more adaptivefilters, each of the one or more adaptive filters configured to receivea corrupted signal and to produce an error signal; one or more adaptivefilter controllers stored in memory, each corresponding to one of theone or more adaptive filters and each configured to halt adaptation ofits corresponding adaptive filter according to a cross-correlation ofits corresponding corrupted signal and its corresponding error signal ofits corresponding adaptive filter, the one or more adaptive filtercontrollers are configured to employ a decision statistic to haltadaptation of their corresponding adaptive filters, the decisionstatistic being based in part on a ratio of an estimate of a time domaincross-correlation coefficient between the corrupted signal and the errorsignal and an estimate of a time domain variance of the corruptedsignal.
 2. The system of claim 1, wherein the decision statistic is afrequency domain decision statistic.
 3. The system of claim 2, whereinthe frequency domain decision statistic is${1 - \frac{{\hat{r}}_{em}( {k,t} )}{{\hat{\sigma}}_{m}^{2}( {k,t} )}},$where {circumflex over (r)}_(em)(k,t) is an estimate of across-correlation coefficient between the corrupted signal and the errorsignal for a kth frequency subband of the tth frame and {circumflex over(σ)}_(m) ²(k,t) is an estimate of a variance of the corrupted signal forthe kth frequency subband of the tth frame.
 4. The system of claim 1,wherein the decision statistic comprises a noise term.
 5. The system ofclaim 1, wherein the decision statistic is${1 - \frac{{{\hat{r}}_{em}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}{{\hat{\sigma}}_{m}^{2}( {k,t} )}},$where {circumflex over (r)}_(em)(k,t) is an estimate of across-correlation coefficient between the corrupted signal and the errorsignal for a kth frequency subband and tth frame, {circumflex over(σ)}_(m) ²(k,t) is an estimate of a variance of the corrupted signal forthe kth frequency subband and tth frame and {circumflex over (σ)}_(z)²(k,t) is a variance of the noise in the kth subband and the tth frame.6. The system of claim 4, wherein the noise term is an estimate of noisein a kth frequency sub and of the corrupted signal.
 7. The system ofclaim 1, wherein the decision statistic is an overall decisionstatistic.
 8. The system of claim 7, wherein the overall decisionstatistic is based on individual decision statistics corresponding toeach of the one or more cross-correlation controllers. 9.Computer-executable instructions for performing a method of controllingone or more adaptive filters of an echo canceller, thecomputer-executable instructions stored on one or more computer-readablemedia, the method comprising: receiving a corrupted signal; receivingone or more cancellation errors from the one or more adaptive filters ofthe echo canceller; computing one or more decision statistics based inpart on a ratio of an estimate of a time domain cross-correlation of thecorrupted signal and the one or more cancellation errors and an estimateof a time domain variance of the corrupted signal; comparing the one ormore decision statistic to their threshold values; and controlling theone or more adaptive filters based on the comparison of the one or moredecision statistics to their threshold values.
 10. Thecomputer-executable instructions of claim 9, wherein computing the oneor more decision statistics further includes a plurality of frequencysubbands and wherein comparing the decision statistic to the thresholdvalue comprises comparing each of the plurality of decision statisticsto its threshold value.
 11. The computer-executable instructions ofclaim 9, wherein the one or more decision statistics are given by${1 - \frac{{{\hat{r}}_{em}( {k,t} )} - {{\hat{\sigma}}_{z}^{2}( {k,t} )}}{{\hat{\sigma}}_{m}^{2}( {k,t} )}},$where {circumflex over (r)}_(em)(k,t) is an estimate of across-correlation coefficient between the corrupted signal and the errorsignal for a kth frequency subband and tth frame, {circumflex over(σ)}_(m) ²(k,t) is an estimate of a variance of the corrupted signal forthe kth frequency subband and tth frame and {circumflex over (σ)}_(z)²(k,t) is a variance of the noise in the kth subband and the tth frame.12. The computer-executable instructions of claim 9, wherein the one ormore decision statistics are given by${1 - \frac{{\hat{r}}_{em}( {k,t} )}{{\hat{\sigma}}_{m}^{2}( {k,t} )}},$where {circumflex over (r)}_(em)(k,t) is an estimate of across-correlation coefficient between the corrupted signal and the errorsignal for a kth frequency subband of the tth frame and ({circumflexover (σ)}_(m) ²(k,t) is an estimate of a variance of the corruptedsignal for the kth frequency subband of the tth frame.
 13. Thecomputer-executable instructions of claim 9, wherein controlling the oneor more adaptive filters based on the comparison of the one or moredecision statistics to their threshold values comprises employing anoverall decision statistic.
 14. The computer-executable instructions ofclaim 9, wherein computing the one or more decision statistics based onthe cross-correlation of the corrupted signal and the one or morecancellation errors comprises employing an exponential recursiveweighting algorithm.
 15. A system for facilitating communication,comprising: means for cancelling an echo in a corrupted signal, themeans for cancelling the echo comprising one or more adaptive filtersstored in memory; and means for stopping the one or more adaptivefilters from adapting based in part on a ratio of an estimate of a timedomain cross-correlation of the corrupted signal with a residual signalproduced by the means for cancelling the echo and an estimate of a timedomain variance of the corrupted signal.
 16. The system of claim 15,wherein the means for stopping is implemented in the frequency domain.17. The system of claim 15, wherein the means for stopping comprises adecision statistic.
 18. The system of claim 17, wherein the decisionstatistic is an overall decision statistic.